Ranking Fractured Reservoir Models Using Flow Diagnostics

Abstract

Abstract This paper describes the application and testing of innovative dual porosity flow diagnostics to quantitatively rank large ensembles of fractured reservoir models. Flow diagnostics can approximate the dynamic response of multi-million cell models in seconds on standard hardware. The need for new faster screening methods stems from the challenge of making robust forecasts for naturally fractured carbonate reservoirs. First order uncertainties including the distribution and properties of natural fractures, matrix heterogeneity and wettability can all negatively impact on recovery. A robust multi-realisation approach to production forecasting is often rendered impractical due to the time cost for simulating many models. We have extended existing flow diagnostics techniques to dual porosity systems by accounting for the matrix-fracture exchange. New metrics combine the transfer rate with the advective time of flight in the fractures identifying risk factors for early water breakthrough and providing quantitative measures of dynamic heterogeneity. We have compared ranking a large ensemble of synthetic fractured reservoir models using dual porosity flow diagnostics and using full-physics simulation. The synthetic ensemble explores a number of different geological concepts around the fracture distributions, wettability and matrix heterogeneity which can. Not only does the flow diagnostic ranking agree well with the cumulative oil ranking the run time for the flow diagnostics is <0.25% of the total simulation time. This significant reduction in the time to compare models allows more time to spend running full physics simulation on the important and geologically diverse cases that offer the most insight.